Data Manipulation in Python - Master Python, NumPy, and Pandas

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Meta Brains

3:47:17

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00001 Welcome to the course.mp4

00:38

00002 Introduction to Python.mp4

00:53

00003 Setting up Python.mp4

02:24

00004 What is Jupyter.mp4

00:59

00005 Anaconda Installation - Windows Mac and Ubuntu.mp4

04:15

00006 How to Implement Python in Jupyter.mp4

00:44

00007 Managing Directories in Jupyter Notebook.mp4

02:48

00008 Input Output.mp4

01:44

00009 Working with Different Datatypes.mp4

01:05

00010 Variables.mp4

01:50

00011 Arithmetic Operators.mp4

01:48

00012 Comparison Operators.mp4

00:43

00013 Logical Operators.mp4

03:05

00014 Conditional Statements.mp4

02:20

00015 Loops.mp4

04:33

00016 Sequences - Lists.mp4

03:18

00017 Sequences - Dictionaries.mp4

02:48

00018 Sequences - Tuples.mp4

01:07

00019 Functions - Built-in Functions.mp4

00:26

00020 Functions - User-Defined Functions.mp4

03:24

00021 Installing Libraries.mp4

00:36

00022 Importing Libraries.mp4

01:49

00023 Pandas Library for Data Science.mp4

00:49

00024 NumPy Library for Data Science.mp4

00:51

00025 Pandas versus NumPy.mp4

00:33

00026 Matplotlib Library for Data Science.mp4

00:38

00027 Seaborn Library for Data Science.mp4

00:20

00028 Introduction to NumPy Arrays.mp4

00:45

00029 Creating NumPy Arrays.mp4

06:13

00030 Indexing NumPy Arrays.mp4

05:45

00031 Array Shape.mp4

00:35

00032 Iterating Over NumPy Arrays.mp4

05:07

00033 Basic NumPy Arrays - zeros.mp4

01:33

00034 Basic NumPy Arrays - ones.mp4

01:11

00035 Basic NumPy Arrays - full.mp4

01:16

00036 Adding a Scalar.mp4

01:41

00037 Subtracting a Scalar.mp4

01:04

00038 Multiplying by a Scalar.mp4

01:17

00039 Dividing by a Scalar.mp4

01:25

00040 Raise to a Power.mp4

00:48

00041 Transpose.mp4

00:48

00042 Element-Wise Addition.mp4

02:00

00043 Element-Wise Subtraction.mp4

00:56

00044 Element-Wise Multiplication.mp4

00:58

00045 Element-Wise Division.mp4

01:04

00046 Matrix Multiplication.mp4

01:33

00047 Statistics.mp4

02:56

00048 What is a Python Pandas DataFrame.mp4

00:58

00049 What is a Python Pandas Series.mp4

00:42

00050 DataFrame versus Series.mp4

00:28

00051 Creating a DataFrame Using Lists.mp4

03:17

00052 Creating a DataFrame Using a Dictionary.mp4

01:06

00053 Loading CSV Data into Python.mp4

01:52

00054 Changing the Index Column.mp4

01:06

00055 Inplace.mp4

01:20

00056 Examining the DataFrame - Head and Tail.mp4

00:36

00057 Statistical Summary of the DataFrame.mp4

00:37

00058 Slicing Rows Using Bracket Operators.mp4

01:26

00059 Indexing Columns Using Bracket Operators.mp4

00:51

00060 Boolean List.mp4

01:15

00061 Filtering Rows.mp4

01:22

00062 Filtering rows using and Operators.mp4

01:51

00063 Filtering Data Using loc.mp4

03:35

00064 Filtering Data Using iloc.mp4

02:23

00065 Adding and Deleting Rows and Columns.mp4

02:41

00066 Sorting Values.mp4

01:39

00067 Exporting and Saving Pandas DataFrames.mp4

01:29

00068 Concatenating DataFrames.mp4

00:59

00069 Groupby.mp4

02:51

00070 Introduction to Data Cleaning.mp4

00:37

00071 Quality of Data.mp4

00:47

00072 Examples of Anomalies.mp4

01:05

00073 Median-based Anomaly Detection.mp4

02:42

00074 Mean-Based Anomaly Detection.mp4

02:50

00075 Z-Score-Based Anomaly Detection.mp4

02:50

00076 Interquartile Range for Anomaly Detection.mp4

04:33

00077 Dealing with Missing Values.mp4

06:01

00078 Regular Expressions.mp4

06:57

00079 Feature Scaling.mp4

03:18

00080 Introduction.mp4

00:29

00081 Setting Up Matplotlib.mp4

00:33

00082 Plotting Line Plots using Matplotlib.mp4

01:46

00083 Title Labels and Legend.mp4

06:46

00084 Plotting Histograms.mp4

01:22

00085 Plotting Bar Charts.mp4

02:04

00086 Plotting Pie Charts.mp4

02:49

00087 Plotting Scatter Plots.mp4

05:44

00088 Plotting Log Plots.mp4

00:41

00089 Plotting Polar Plots.mp4

02:06

00090 Handling Dates.mp4

00:43

00091 Creating Multiple Subplots in One Figure.mp4

03:38

00092 Introduction.mp4

00:19

00093 What is Exploratory Data Analysis.mp4

00:30

00094 Univariate Analysis.mp4

01:41

00095 Univariate Analysis - Continuous Data.mp4

06:01

00096 Univariate Analysis - Categorical Data.mp4

02:16

00097 Bivariate Analysis - Continuous and Continuous.mp4

04:32

00098 Bivariate Analysis - Categorical and Categorical.mp4

03:07

00099 Bivariate Analysis - Continuous and Categorical.mp4

01:51

00100 Detecting Outliers.mp4

05:34

00101 Categorical Variable Transformation.mp4

04:22

00102 Introduction to Time Series.mp4

02:15

00103 Getting Stock Data Using yfinance.mp4

03:15

00104 Converting a Dataset into Time Series.mp4

04:23

00105 Working with Time Series.mp4

03:49

00106 Time Series Data Visualization with Python.mp4

03:14

Description

Data science is quickly becoming one of the most promising careers in the twenty-first century. It is automated, program-driven, and analytical. As a result, it’s no surprise that the demand for data scientists has been expanding in the job market over the last few years.

We will begin with a quick refresher on Python fundamentals for beginners in this course. This is optional; if you’re already familiar with Python, skip to the next chapter.

Data science will be the topic of the next three sections. We will start with the essential Python libraries for data science, then go on to the fundamental NumPy properties, and lastly begin with mathematics and how to use it in data science.

You will learn about Python Pandas DataFrames and series after learning about data science. Following that, we will get down to business and begin data cleaning. Following that, we will learn how to use Python to visualize data and do data analysis on some sample datasets. Finally, we will cover the Time series in Python and learn how to work with and convert datasets to Time series.

By the end of this course, you will be able to execute data manipulation for data science in Python with ease.

All the resources for this course are available at: https://github.com/PacktPublishing/Data-Manipulation-in-Python---Master-Python-NumPy-and-Pandas

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Python

Data Analysis

Pandas

NumPy

Meta Brains

Instructor's Courses

Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they’re focused on the next great revolution in computing: the Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.